Apply extra Ruff rules

examples/1INFILTR/D.py

@@ -6,11 +6,9 @@
 Diffusivity plot.
 """
 
-import numpy as np
 import matplotlib.pyplot as plt
-
+import numpy as np
 from fronts.D import van_genuchten
-
 from validation import r_unit, t_unit
 
 epsilon = 1e-6

examples/1INFILTR/solve.py

@@ -3,12 +3,10 @@
 """1INFILTR case from Hydrus-1D, horizontal."""
 
 import matplotlib.pyplot as plt
-
+import validation
 from fronts import solve
 from fronts.D import van_genuchten
 
-import validation
-
 epsilon = 1e-6
 
 Ks = 25  # cm/h

examples/1INFILTR/validation.py

@@ -7,13 +7,12 @@
 1001 nodes, water content tolerance = 1e-6
 """
 
-import sys
-import os
 import itertools
+import os
+import sys
 
-import numpy as np
 import matplotlib.pyplot as plt
-
+import numpy as np
 
 r_unit = "cm"
 t_unit = "h"

examples/HF135/D.py

@@ -6,11 +6,9 @@
 Diffusivity plot.
 """
 
-import numpy as np
 import matplotlib.pyplot as plt
-
+import numpy as np
 from fronts.D import van_genuchten
-
 from validation import r_unit, t_unit
 
 epsilon = 1e-7

examples/HF135/inverse1.py

@@ -11,13 +11,11 @@
 Warning: this example takes ~30 seconds to run to completion.
 """
 
-import numpy as np
 import matplotlib.pyplot as plt
-
-from fronts import solve, inverse
-from fronts.D import van_genuchten
-
+import numpy as np
 import validation
+from fronts import inverse, solve
+from fronts.D import van_genuchten
 
 epsilon = 1e-7
 

examples/HF135/inverse2.py

@@ -10,10 +10,8 @@
 """
 
 import matplotlib.pyplot as plt
-
-from fronts import solve, inverse, o
-
 import validation
+from fronts import inverse, o, solve
 
 D_inverse = inverse(o=o(validation.r, validation.t)[::5], samples=validation.theta[::5])
 # Using only a fifth of the points so that it does not run too slow

examples/HF135/radial.py

@@ -4,14 +4,12 @@
 Radial flow in a HF135 nitrocellulose membrane.
 """
 
-import numpy as np
-import matplotlib.pyplot as plt
-
 from math import pi
 
+import matplotlib.pyplot as plt
+import numpy as np
 from fronts import solve_flowrate
 from fronts.D import van_genuchten
-
 from validation import r_unit, t_unit
 
 epsilon = 1e-7

examples/HF135/refine.py

@@ -8,12 +8,10 @@
 """
 
 import matplotlib.pyplot as plt
-
+import validation
 from fronts import solve, solve_from_guess
 from fronts.D import van_genuchten
 
-import validation
-
 epsilon = 1e-7
 
 # Wetting of an HF135 membrane, Van Genuchten model

examples/HF135/solve.py

@@ -8,12 +8,10 @@
 """
 
 import matplotlib.pyplot as plt
-
+import validation
 from fronts import solve
 from fronts.D import van_genuchten
 
-import validation
-
 epsilon = 1e-7
 
 # Wetting of an HF135 membrane, Van Genuchten model

examples/HF135/validation.py

@@ -9,11 +9,11 @@
 
 """
 
-import sys
 import os
+import sys
 
-import numpy as np
 import matplotlib.pyplot as plt
+import numpy as np
 
 _filename = os.path.join(sys.path[0], "groundwaterFoam_results.csv")
 

examples/exact/D.py

@@ -2,8 +2,8 @@
 
 """Plot of D for the 'exact' validation case."""
 
-import numpy as np
 import matplotlib.pyplot as plt
+import numpy as np
 
 
 def D(theta):

examples/exact/fromguess.py

@@ -5,9 +5,8 @@
 `fronts.solve_from_guess`) and compares the solutions.
 """
 
-import numpy as np
 import matplotlib.pyplot as plt
-
+import numpy as np
 from fronts import solve_from_guess
 
 o = np.linspace(0, 20, 100)

examples/exact/solve.py

@@ -5,9 +5,8 @@
 and compares the solutions.
 """
 
-import numpy as np
 import matplotlib.pyplot as plt
-
+import numpy as np
 from fronts import solve
 
 theta = solve(D="0.5*(1 - log(theta))", i=0, b=1, verbose=2)

examples/powerlaw/D.py

@@ -4,9 +4,8 @@
 Plot of D in the powerlaw case.
 """
 
-import numpy as np
 import matplotlib.pyplot as plt
-
+import numpy as np
 from fronts.D import power_law
 
 k = 4.0

examples/powerlaw/inverse.py

@@ -2,10 +2,9 @@
 
 """Examples of usage of `fronts.solve` and `fronts.inverse`."""
 
-import numpy as np
 import matplotlib.pyplot as plt
-
-from fronts import solve, inverse
+import numpy as np
+from fronts import inverse, solve
 from fronts.D import power_law
 
 k = 4.0

examples/powerlaw/solve.py

@@ -2,13 +2,11 @@
 
 """Example of usage of `fronts.solve`."""
 
-import numpy as np
 import matplotlib.pyplot as plt
-
+import numpy as np
 from fronts import solve
 from fronts.D import power_law
 
-
 k = 4.0
 ci = 0.1
 cb = 1.0

fronts/D.py

@@ -1,5 +1,3 @@
-# -*- coding: utf-8 -*-
-
 """D functions."""
 
 import functools
@@ -42,7 +40,6 @@ def constant(D0):
     numerical solvers are necessary. However, it is provided here given that it
     is the simplest supported function.
     """
-
     if D0 <= 0:
         raise ValueError("D0 must be positive")
 
@@ -103,7 +100,6 @@ def from_expr(expr, vectorized=True, max_derivatives=2):
     Users will rarely need to call this function, as all built-in solver
     functions already do so themselves when they receive an expression as `D`.
     """
-
     expr = sympy.sympify(expr)
 
     free = expr.free_symbols
@@ -181,7 +177,7 @@ def D(theta, derivatives=0):
                 return f01(theta)
 
             if derivatives == 2 and max_derivatives == 2:
-                return f01(theta) + [f2(theta)]
+                return (*f01(theta), f2(theta))
 
             raise ValueError(
                 f"derivatives must be one of {{{', '.join(str(n) for n in range(max_derivatives+1))}}}"
@@ -251,7 +247,9 @@ def D(theta, derivatives=0):
 
 def _as_Ks(Ks=None, k=None, nu=1e-6, g=9.81):
     r"""
-    Return the saturated hydraulic conductivity, computed from the instrinsic
+    Return the saturated hydraulic conductivity.
+
+    The saturated hydraulic conductivity is computed from the instrinsic
     permeability if necessary.
 
     Parameters
@@ -372,10 +370,9 @@ def brooks_and_corey(
 
     References
     ----------
-    [1] BROOKS, R.; COREY, T. Hydraulic properties of porous media. Hydrology
+    [1] BROOKS, R.; COREY, T. Hydraulic properties of porous media. Hydrology
     Papers, Colorado State University, 1964, vol. 24, p. 37.
     """
-
     if alpha <= 0:
         raise ValueError("alpha must be positive")
 
@@ -496,11 +493,10 @@ def van_genuchten(
 
     References
     ----------
-    [1] VAN GENUCHTEN, M. Th. A closed-form equation for predicting the
+    [1] VAN GENUCHTEN, M. Th. A closed-form equation for predicting the
     hydraulic conductivity of unsaturated soils. Soil Science Society of
     America Journal, 1980, vol. 44, no 5, p. 892-898.
     """
-
     if n is not None:
         if m is not None:
             raise TypeError("cannot pass both n and m")
@@ -605,6 +601,8 @@ def letxs(
     theta_range=(0.0, 1.0),
 ):
     r"""
+    Return a LETx + LETs diffusivity function.
+
     Return a diffusivity function that combines the LETx relative permeability
     correlation and the LETs capillary pressure correlation for spontaneous
     imbibition. Both correlations are part of the LET family of hydraulic
@@ -699,7 +697,6 @@ def letxs(
     capillary imbibition models in paper-based microfluidic applications.
     Transport in Porous Media, 2022, vol. 141, no. 7, pp. 1-20.
     """
-
     Ks = _as_Ks(Ks=Ks, k=k, nu=nu, g=g)
 
     # - Code generated with functionstr() from ../symbolic/generate.py - #
@@ -997,7 +994,6 @@ def richards(C, kr, Ks=None, k=None, nu=1e-6, g=9.81):
         In all cases, the argument ``theta`` may be a single float or a NumPy
         array.
     """
-
     Ks = _as_Ks(Ks=Ks, k=k, nu=nu, g=g)
 
     def D(theta, derivatives=0):

fronts/__init__.py

@@ -1,13 +1,10 @@
-"""
-Numerical library for nonlinear diffusion problems based on the Boltzmann
-transformation.
-"""
+"""Numerical library for nonlinear diffusion problems based on the Boltzmann transformation."""
 
 __version__ = "1.2.2"
 
-from ._boltzmann import ode, BaseSolution, o, do_dr, do_dt, r, t, as_o
-from ._semiinfinite import solve, solve_flowrate, solve_from_guess, Solution
+from ._boltzmann import BaseSolution, as_o, do_dr, do_dt, o, ode, r, t
 from ._inverse import inverse, sorptivity
+from ._semiinfinite import Solution, solve, solve_flowrate, solve_from_guess
 
 __all__ = [
     "solve",